Abstract: An information processing apparatus includes a memory; and a processor coupled to the memory and the processor configured to: generate phoneme string information in which a plurality of phonemes included in voice information is arranged in time series, based on a recognition result of the phonemes for the voice information; and learn parameters of a network such that when the phoneme string information is input to the network, output information that is output from the network approaches correct answer information that indicates whether a predetermined conversation situation is included in the voice information that corresponds to the phoneme string information.

Abstract: An information processing apparatus includes a memory; and a processor coupled to the memory and the processor configured to: generate phoneme string information in which a plurality of phonemes included in voice information is arranged in time series, based on a recognition result of the phonemes for the voice information; and learn parameters of a network such that when the phoneme string information is input to the network, output information that is output from the network approaches correct answer information that indicates whether a predetermined conversation situation is included in the voice information that corresponds to the phoneme string information.

Abstract: A learning device includes: a memory; and a processor coupled to the memory and configured to: generate plural first subsets of time-series data by dividing time-series data into predetermined intervals, the time-series data including plural sets of data arranged in time series, and generate first learning data including each of the plural first subsets of time-series data associated with teacher data corresponding to the whole time-series data; learn, based on the first learning data, a first parameter of a first RNN of recurrent neural networks (RNNs), included in plural layers, the first RNN being included in a first layer; and set the learned first parameter for the first RNN, and learn, based on data and the teacher data, parameters of the RNNs included in the plural layers, the data being acquired by input of each of the first subsets of time-series data into the first RNN.

Abstract: A voice recognition method includes: detecting a vocal section including a vocal sound in a voice, based on a feature value of an audio signal representing the voice; identifying a word expressed by the vocal sound in the vocal section, by matching the feature value of the audio signal of the vocal section and an acoustic model of each of a plurality of words; and selecting, with a processor, the word expressed by the vocal sound in a word section based on a comparison result between a signal characteristic of the word section and a signal characteristic of the vocal section.

Abstract: An utterance state detection device includes an user voice stream data input unit that gets user voice stream data of an user, a frequency element extraction unit that extracts high frequency elements by frequency-analyzing the user voice stream data, a fluctuation degree calculation unit that calculates a fluctuation degree of the high frequency elements thus extracted every unit time, a statistic calculation unit that calculates a statistic every certain interval based on a plurality of the fluctuation degrees in a certain period of time, and an utterance state detection unit that detects an utterance state of a specified user based on the statistic obtained from user voice stream data of the specified user.

Abstract: A recognizing device includes a memory and a processor coupled to the memory. The memory stores words included in a text and positional information indicating a position of the words in the text. The processor executes a process including comparing an input voice signal with reading information of a character string that connects a plurality of words stored in the memory to calculate a similarity; calculating a connection score indicating a proximity between the plurality of connected words based on positional information of the words stored in the memory; and determining a character string corresponding to the voice signal based on the similarity and the connection score.

Abstract: A speech recognition device includes, a speech recognition section that conducts a search, by speech recognition, on audio data stored in a first memory section to extract word-spoken portions where plural words transferred are each spoken and, of the word-spoken portions extracted, rejects the word-spoken portion for the word designated as a rejecting object; an acquisition section that obtains a derived word of a designated search target word, the derived word being generated in accordance with a derived word generation rule stored in a second memory section or read out from the second memory section; a transfer section that transfers the derived word and the search target word to the speech recognition section, the derived word being set to the outputting object or the rejecting object by the acquisition section; and an output section that outputs the word-spoken portion extracted and not rejected in the search.

Abstract: A speech recognition apparatus includes a speech collating unit that calculates similarities at each time between a feature amount converted by a speech analyzing unit and a word model generated by a word model generating unit. The speech collating unit extracts a word model from word models generated by the word model generating unit, whose minimum similarity among similarities at each time or whose overall similarity obtained from similarities at each time satisfies a second threshold value condition, and whose similarity at each time in a section among vocalization sections of utterance speech and corresponding to either a phoneme or a phoneme string associated with a first threshold value condition satisfies the first threshold value condition, and outputs as a recognition result the recognized word corresponding to the extracted word model.

Abstract: A spoken term detection apparatus includes: processing performed by a processor includes a feature extraction process extracting an acoustic feature from speech data accumulated in an accumulation part and storing an extracted acoustic feature in an acoustic feature storage, a first calculation process calculating a standard score from a similarity between an acoustic feature stored in the acoustic feature storage and an acoustic model stored in the acoustic model storage part, a second calculation process for comparing an acoustic model corresponding to an input keyword with the acoustic feature stored in the acoustic feature storage part to calculate a score of the keyword, and a retrieval process retrieving speech data including the keyword from speech data accumulated in the accumulation part based on the score of the keyword calculated by the second calculation process and the standard score stored in the standard score storage part.

Abstract: A voice recognition method includes: detecting a vocal section including a vocal sound in a voice, based on a feature value of an audio signal representing the voice; identifying a word expressed by the vocal sound in the vocal section, by matching the feature value of the audio signal of the vocal section and an acoustic model of each of a plurality of words; and selecting, with a processor, the word expressed by the vocal sound in a word section based on a comparison result between a signal characteristic of the word section and a signal characteristic of the vocal section.

Abstract: A recognizing device includes a memory and a processor coupled to the memory. The memory stores words included in a sentence and positional information indicating a position of the words in the sentence. The processor executes a process including comparing an input voice signal with reading information of a character string that connects a plurality of words stored in the memory to calculate a similarity; calculating a connection score indicating a proximity between the plurality of connected words based on positional information of the words stored in the memory; and determining a character string corresponding to the voice signal based on the similarity and the connection score.

Abstract: A speech recognition apparatus for recognizing a plurality of sequential words contained in a speech includes an acoustic model reading part for reading an acoustic model, a dictionary management part for reading required data from dictionary data, and a recognition part for successively recognizing the sequential words by matching a group of words represented by the dictionary data with the inputted speech, using the acoustic model, wherein the dictionary data contains a beginning part dictionary representing beginning parts of words, and a group of ending part dictionaries storing data representing ending parts, the ending part dictionary and/or the beginning part dictionary are read in accordance with the word recognized by the recognition part, and the recognition part matches a subsequent speech with the beginning parts of words contained in the beginning part dictionary while the dictionary management part is reading the ending part dictionary and/or the beginning part dictionary.

Abstract: A speech recognition device includes, a speech recognition section that conducts a search, by speech recognition, on audio data stored in a first memory section to extract word-spoken portions where plural words transferred are each spoken and, of the word-spoken portions extracted, rejects the word-spoken portion for the word designated as a rejecting object; an acquisition section that obtains a derived word of a designated search target word, the derived word being generated in accordance with a derived word generation rule stored in a second memory section or read out from the second memory section; a transfer section that transfers the derived word and the search target word to the speech recognition section, the derived word being and set to the outputting object or the rejecting object by the acquisition section; and an output section that outputs the word-spoken portion extracted and not rejected in the search.

Abstract: A voice recognition apparatus includes an extraction unit extracting a feature amount from a voice signal, a word dictionary storing a plurality of recognition words; a reject word generation unit storing reject words in the word dictionary in association with the recognition words and a collation unit calculating a degree of similarity between the voice signal and each of the recognition words and reject words stored in the word dictionary by using the feature amount extracted by the extraction unit, determining whether or not a word having a high calculated degree of similarity corresponds to a reject word, when the word is determined as the reject word, excluding the recognition word stored in the word dictionary in association with the reject word from a result of recognition, and outputting a recognition word having a high calculated degree of similarity as a result of recognition.

Abstract: An utterance state detection device includes an user voice stream data input unit that gets user voice stream data of an user, a frequency element extraction unit that extracts high frequency elements by frequency-analyzing the user voice stream data, a fluctuation degree calculation unit that calculates a fluctuation degree of the high frequency elements thus extracted every unit time, a statistic calculation unit that calculates a statistic every certain interval based on a plurality of the fluctuation degrees in a certain period of time, and an utterance state detection unit that detects an utterance state of a specified user based on the statistic obtained from user voice stream data of the specified user.

Abstract: A spoken term detection apparatus includes: processing performed by a processor includes a feature extraction process extracting an acoustic feature from speech data accumulated in an accumulation part and storing an extracted acoustic feature in an acoustic feature storage, a first calculation process calculating a standard score from a similarity between an acoustic feature stored in the acoustic feature storage and an acoustic model stored in the acoustic model storage part, a second calculation process for comparing an acoustic model corresponding to an input keyword with the acoustic feature stored in the acoustic feature storage part to calculate a score of the keyword, and a retrieval process retrieving speech data including the keyword from speech data accumulated in the accumulation part based on the score of the keyword calculated by the second calculation process and the standard score stored in the standard score storage part.

Abstract: A speech recognition system includes the following: a feature calculating unit; a sound level calculating unit that calculates an input sound level in each frame; a decoding unit that matches the feature of each frame with an acoustic model and a linguistic model, and outputs a recognized word sequence; a start-point detector that determines a start frame of a speech section based on a reference value; an end-point detector that determines an end frame of the speech section based on a reference value; and a reference value updating unit that updates the reference value in accordance with variations in the input sound level. The start-point detector updates the start frame every time the reference value is updated. The decoding unit starts matching before being notified of the end frame and corrects the matching results every time it is notified of the start frame. The speech recognition system can suppress a delay in response time while performing speech recognition based on a proper speech section.

Abstract: When the degree of similarity of the recognition candidates is greater than the second threshold value, the speech verification unit outputs the recognition candidates as a recognition result, and when the degree of similarity of the recognition candidates is smaller than the second threshold value, it outputs the recognition candidates as a recognition result if the degree of similarity of the recognition candidates is greater than the first threshold value and, at the same time, the degree of similarity of the recognition candidates is greater than the degree of similarity of the rejection candidates. It should be noted that the first threshold value is a measure used for rejecting input speech. The second threshold value is larger than the first threshold value and is used as a measure for outputting recognition candidates as a recognition result.

Abstract: A speech recognition apparatus includes a speech collating unit that calculates similarities at each time between a feature amount converted by a speech analyzing unit and a word model generated by a word model generating unit. The speech collating unit extracts a word model from word models generated by the word model generating unit, whose minimum similarity among similarities at each time or whose overall similarity obtained from similarities at each time satisfies a second threshold value condition, and whose similarity at each time in a section among vocalization sections of utterance speech and corresponding to either a phoneme or a phoneme string associated with a first threshold value condition satisfies the first threshold value condition, and outputs as a recognition result the recognized word corresponding to the extracted word model.

Abstract: A voice recognition system and a voice processing system in which a self-repair utterance can be inputted and recognized accurately, as in a conversation in which a human user makes a self-repair utterance. A signal processing unit converts speech voice data into a feature, a voice section detecting unit detects voice sections in the speech voice data, and a priority determining unit selects a voice section that includes a self-repair utterance from among the voice sections according to a priority criterion without using any result of recognizing a speech vocabulary sequence. Priority criteria can include a length of the voice section, signal to noise ratio, chronological order of the voice section as well as speech speed. A decoder calculates a matching score with a recognition vocabulary using the feature of the voice section and an acoustic model.